Name
Abstract | ||
|---|---|---|
Hybrid set of optimally trained feed-forward, Hopfield and Elman neural networks were used as computational tools and were applied to immunoinformatics. These neural networks enabled a better understanding of the functions and key components of the adaptive immune system. A functional block representation was also created in order to summarize the basic adaptive immune system and the appropriate neural networks were employed to solve them. Training and learning accuracy of all neural networks were very good. Polymorphism, inheritance and encapsulation (PIE) learning concepts were adopted in order to predict the static and temporal behavior of adaptive immune system interactions in response to typical virus attacks. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CIMCA.2005.1631302 | CIMCA/IAWTIC |
Keywords | Field | DocType |
adaptive immune system,functional block representation,elman neural network,appropriate neural network,neural network,better understanding,computational tool,hybrid neural networks,hybrid set,basic adaptive immune system,adaptive immune system interaction,learning artificial intelligence,feed forward neural network,polymorphism,feed forward,adaptive immunity | Feedforward neural network,Computer science,Types of artificial neural networks,Artificial intelligence,Artificial neural network,Encapsulation (computer programming),Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2504-0-01 | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khrizel B. Solano | 1 | 0 | 0.34 |
| Tolja Djekovic | 2 | 0 | 0.34 |
| Mohamed Zohdy | 3 | 1 | 1.38 |